Algorithmic trading, or the related term high frequency trading, is generally defined as the usage of computers to generate orders that are entered on marketplaces. Lately the term, rightly or wrongly, has also been widely associated with its negative impact on market places from a technical as well as business perspective.
Regardless of this it is a fact that algorithmic trading in various forms has become increasingly popular on most of the major market places around the world.
Some algorithms of algorithmic trading provide liquidity, but if looked upon in isolation they may have a negative effect on the market place as a whole.
Some of the negative effects are as follows:                1. Increased transaction flow (by design or by mistake)        2. May discourage market makers        3. May discourage institutional investors        
Some algorithms produce a high transaction flow. This implies that the market place needs to scale its system in order to being able to handle the increased transaction flow.
As a special case one also needs to consider the case of runaway algorithms, not as uncommon as one might think, that produces an irrational amount of transactions i.e. far beyond anything motivated by the underlying algorithm.
Market makers may be discouraged by algorithms that take advantage of weaknesses in the market maker applications. An example might be an algorithm that attempts to outwit a market maker application in a warrant market by simply being faster. By reacting faster to changes in underlying prices than the market maker application the algorithm can trade at stale market maker prices, thus making a profit.
In other words what is occurring is an “arms race” between algorithms of the market maker, and algorithms that tries to take advantage of weaknesses in the market maker applications, the sniper.
Note that it is possible to reason in two ways about the above warrant market maker example. One is that the algorithm deployed by the sniper is removing inefficiencies in the market; the other is that it will force the market maker to widen his spread or indeed stop making a market, thus reducing the liquidity of the market place.
Theoretically the correct way of reasoning is probably that the sniper is helping to make sure that the market is as efficient as possible. In a “real” market the sniper might reduce liquidity by scaring away market makers.
Some institutional investors feel that some algorithms take unfair advantage of the information leakage inherent when executing large orders.
A market place should preferably, on a continuous basis, weigh the benefits of the extra liquidity created by algorithmic trading against the above listed negative effects. A pre-requisite for doing this is not only to understand which part of the total transaction flow that originates from algorithmic trading but also its characteristics in terms of burstiness, order/trade sizes, average order life spans etc. since the cost of handling a transaction flow is significantly affected by these types of characteristics. A market place system therefore needs to include the proper tools facilitating such an analysis.
Hence, there is a need for methods, apparatus and systems for alleviating any negative impacts of algorithmic trading on the marketplaces.
Furthermore, in prior art, different market participants have different ability to act on new information, e.g. because of what latency conditions they are working with.
Market participants accessing an electronic marketplace experience at least two types of latency:
1. Market data latency: the time it takes for market data disseminated by the marketplace system to reach the market participant.
2. Order entry latency: the time it takes for a market participant to enter an order on the marketplace and receive an acknowledgement of its initial matching status.
The source of both of these types of latency is multifaceted and is affected by a number of factors, such as geographical location of the market participant, Hardware and software deployed by the market participant, hardware and software deployed by the marketplace operator, etc.
In many cases both types of latency tend to be differentiated among market participants, implying that some market participants systematically have faster access to a marketplace than others. Having faster access to a marketplace system than competitors is generally considered to be an important advantage.
For example, consider an order entered onto a marketplace that offers some sort of arbitrage opportunity. The market participant who first receives this information (latency type 1 above) is able to act on it (latency type 2 above) and exploit the arbitrage opportunity.
Hence, if more equal ability among market participants to act on new information is desired, there is a need for methods, apparatus and systems for alleviating effects of latency.